I have been selected Marie Curie Fellow of the week! Marie Sklodowska-Curie Actions (MSCA) Individual Fellowships are highly prestigious and competitive and are meant to support the best, most promising European researchers.

When given a single frame of the video, humans can not only interpret the content of the scene, but also they are able to forecast the near future. This ability is mostly driven by their rich prior knowledge about the visual world, both in terms of (i) the dynamics of moving agents, as well as (ii) the semantic of the scene. We exploit the interplay between these two key elements to predict scene-specific motion patterns.

In the past two weeks I have been involved as computer vision project mentor in the Stanford Artificial Intelligence Laboratory’s OutReach Summer program (SAILORS). SAILORS is a summer camp for high school girls and it is intended to increase diversity in the field of AI. SAILORS aims to teach technically rigorous AI concepts in the context of societal impact.

Vision and social media has recently become a very active inter-disciplinary research area, involving computer vision, multimedia, machine learning, and data mining. This workshop aims to bring together researchers in the related fields to promote new research directions for problems involving vision and social media, such as large-scale visual content analysis, search and mining.

Everything you wanted to know about image tagging, tag refinement and social image retrieval. Our paper has been (finally) accepted to ACM Computing Surveys! This is a titanic effort, by Xirong Li, Tiberio Uricchio, myself, Marco Bertini, Cees Snoek and Alberto Del Bimbo, to structure the growing literature in the field, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations.

Some images that are difficult to recognize on their own may become more clear in the context of a neighborhood of related images with similar social-network metadata. We build on this intuition to improve multilabel image annotation. Our model uses image metadata nonparametrically to generate neighborhoods of related images using Jaccard similarities, then uses a deep neural network to blend visual information from the image and its neighbors.

We gave a tutorial on “Image Tag Assignment, Refinement and Retrieval” at ACM MM 2015, based on our recent survey. Our tutorial focuses on challenges and solutions for content-based image retrieval in the context of online image sharing and tagging. We present a unified review on three closely linked problems: tag assignment, tag refinement, and tag-based image retrieval. We introduce a taxonomy to structure the growing literature, understand the ingredients of the main works, and recognize their merits and limitations.